Energy AI & Optimization Researcher

The role

Test charging models against site constraints

We are studying how charging demand, electrical capacity and flexible schedules interact. The work involves experiments with explicit constraints, baselines and uncertainty rather than claims about untested performance.

Potential contributions

What you'd work on

• Charging-demand analysis, optimization and digital-twin experiments
• Experiment design, measurable baselines and uncertainty-aware evaluation
• Research collaboration around grid integration and distributed charging
• Explore capacity constraints, forecasting assumptions and interpretable optimisation approaches.
• Help translate research questions into measurable pilot hypotheses and evidence reports.

Your background

Relevant experience

Background in energy systems, applied ML, operations research or data-driven modeling.

• Experience with applied optimisation, energy systems modelling or scientific machine learning.
• Ability to design reproducible experiments with clear baselines and uncertainty treatment.
• Good scientific writing and comfort separating research-stage findings from deployed functionality.

If your experience overlaps with this role, send examples of work you've done. You don't need to cover every item.

The team

Working with Veevex

Veevex is based in Estonia and is preparing pilots for private AC charger sharing. Our work covers charger protocols, access permissions, session records and site operations.

Tell us whether you are interested in employment, a project role or research collaboration. Location and working arrangements are discussed individually.